cmdstan_model: Create a new CmdStanModel object

In stan-dev/cmdstanr: R Interface to 'CmdStan'

Create a new CmdStanModel object

Description

Create a new CmdStanModel object from a file containing a Stan program or from an existing Stan executable. The CmdStanModel object stores the path to a Stan program and compiled executable (once created), and provides methods for fitting the model using Stan's algorithms.

See the compile and ... arguments for control over whether and how compilation happens.

Usage

cmdstan_model(stan_file = NULL, exe_file = NULL, compile = TRUE, ...)

Arguments

stan_file	(string) The path to a .stan file containing a Stan program. The helper function write_stan_file() is provided for cases when it is more convenient to specify the Stan program as a string. If stan_file is not specified then exe_file must be specified.
exe_file	(string) The path to an existing Stan model executable. Can be provided instead of or in addition to stan_file (if stan_file is omitted some CmdStanModel methods like ⁠$code()⁠ and ⁠$print()⁠ will not work). This argument can only be used with CmdStan 2.27+.
compile	(logical) Do compilation? The default is TRUE. If FALSE compilation can be done later via the $compile() method.
...	Optionally, additional arguments to pass to the $compile() method if compile=TRUE. These options include specifying the directory for saving the executable, turning on pedantic mode, specifying include paths, configuring C++ options, and more. See $compile() for details.

Value

A CmdStanModel object.

See Also

install_cmdstan(), $compile(), $check_syntax()

The CmdStanR website (mc-stan.org/cmdstanr) for online documentation and tutorials.

The Stan and CmdStan documentation:

• Stan documentation: mc-stan.org/users/documentation

• CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide

Examples

## Not run: 
library(cmdstanr)
library(posterior)
library(bayesplot)
color_scheme_set("brightblue")

# Set path to CmdStan
# (Note: if you installed CmdStan via install_cmdstan() with default settings
# then setting the path is unnecessary but the default below should still work.
# Otherwise use the `path` argument to specify the location of your
# CmdStan installation.)
set_cmdstan_path(path = NULL)

# Create a CmdStanModel object from a Stan program,
# here using the example model that comes with CmdStan
file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan")
mod <- cmdstan_model(file)
mod$print()

# Data as a named list (like RStan)
stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))

# Run MCMC using the 'sample' method
fit_mcmc <- mod$sample(
  data = stan_data,
  seed = 123,
  chains = 2,
  parallel_chains = 2
)

# Use 'posterior' package for summaries
fit_mcmc$summary()

# Check sampling diagnostics
fit_mcmc$diagnostic_summary()

# Get posterior draws
draws <- fit_mcmc$draws()
print(draws)

# Convert to data frame using posterior::as_draws_df
as_draws_df(draws)

# Plot posterior using bayesplot (ggplot2)
mcmc_hist(fit_mcmc$draws("theta"))

# For models fit using MCMC, if you like working with RStan's stanfit objects
# then you can create one with rstan::read_stan_csv()
# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())


# Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm)
# and also demonstrate specifying data as a path to a file instead of a list
my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json")
fit_optim <- mod$optimize(data = my_data_file, seed = 123)
fit_optim$summary()

# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
fit_laplace$summary()

# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
fit_vb$summary()
mcmc_hist(fit_vb$draws("theta"))

# Run 'pathfinder' method, a new alternative to the variational method
fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
fit_pf$summary()
mcmc_hist(fit_pf$draws("theta"))

# Run 'pathfinder' again with more paths, fewer draws per path,
# better covariance approximation, and fewer LBFGSs iterations
fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
                         history_size=50, max_lbfgs_iters=100)

# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
  data = stan_data,
  seed = 123,
  chains = 2,
  refresh = 0,
  init = function() list(theta = runif(1))
)
fit_mcmc_w_init_fun_2 <- mod$sample(
  data = stan_data,
  seed = 123,
  chains = 2,
  refresh = 0,
  init = function(chain_id) {
    # silly but demonstrates optional use of chain_id
    list(theta = 1 / (chain_id + 1))
  }
)
fit_mcmc_w_init_fun_2$init()

# Specifying initial values as a list of lists
fit_mcmc_w_init_list <- mod$sample(
  data = stan_data,
  seed = 123,
  chains = 2,
  refresh = 0,
  init = list(
    list(theta = 0.75), # chain 1
    list(theta = 0.25)  # chain 2
  )
)
fit_optim_w_init_list <- mod$optimize(
  data = stan_data,
  seed = 123,
  init = list(
    list(theta = 0.75)
  )
)
fit_optim_w_init_list$init()

## End(Not run)

stan-dev/cmdstanr documentation built on April 26, 2024, 2:02 a.m.